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Project Progress

1) Metabolic cost of compartmentalization (N. Klitgord, D. Segrè)
In this part of the project, we are exploring the role of metabolic compartmentalization in the emergence of complex metabolism. Specifically, by performing in-silico experiments with a genome-scale model of S. cerevisiae, we are asking how the presence of compartments affects metabolic fluxes. Preliminary results indicate the presence of a major energetic cost associated with the presence of compartmentalization. Substantial benefits must therefore balance this cost to justify maintenance of complex internal organism structure.

2) Evolution of metabolic efficiency and the topology of biochemical networks (W. Riehl, D. Segrè)
Metabolic networks perform fundamental functions in living cells, including energy transduction and building block biosynthesis. The topology of several pathways in these networks is highly conserved, though the same reactions may be used for different goals in different conditions and organisms. Towards predicting expected topological features of an optimally evolved metabolic system we build an artificial chemistry that represents a toy biosphere-level metabolic network. By comparing the toy model with real metabolic networks, we obtain intriguing insight on the laws and accidents involved in long-term biochemical adaptation.

3) Expansion of the metabolic and enzyme universe (A. Jayaraman, D. Segrè)
Previous work by Raymond and Segrè (Science, 2006) showed the effect of oxygen on the complexity of metabolic networks using a heuristic referred to as metabolic network expansion. In order to simulate a possible timeline of the metabolic changes associated with the appearance of oxygen in the atmosphere, we are now introducing information about enzyme function. This allows us to simulate more realistic evolutionary processes that can generate predictions on the historical order of appearance of different metabolites and enzymes.

4) Integration of experimental and computational data for improved understanding of yeast metabolism (E. Snitkin and D. Segrè)
Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in the study of metabolic adaptation. We have performed integrated analyses of high-throughput experimental data and genome-scale computational predictions of metabolic phenotypes in yeast mutants (465 deletion mutants under 16 different conditions). In addition to identifying strengths and weaknesses of genome scale models, we could formulate testable hypotheses on specific pathways, such as raffinose and glycerol utilization.